Random Outlier Detection Test Case Generator

Description

Generate a Gaussian baseline plus explicitly labeled fixed-offset contamination, shuffle retained labels and delegate Tukey-IQR outlier counting after safe positive-affine normalization; injected labels are not detector truth and quartiles follow the inherited median-of-halves convention.

Synthetic statistical data separates a specified generating model from the values observed in a finite draw. A model parameter, a sample summary, and the result of a fitted analysis answer different questions; retaining them separately makes numerical and statistical checks easier to interpret.

When to use this tool

Use this generator to create reproducible outlier detection test case cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply baselineCount, baselineMean, baselineStandardDeviation, contaminationCount, contaminationOffset, contaminationSide in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read values, injected, injectedCount, detectedCount. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Draw a Gaussian baseline and inject fixed-offset contaminants on the selected side or sides. Shuffle while retaining injected labels, normalize magnitudes for the delegated IQR detector, and return injected and detected counts separately.^[1]

Delegated operations used for this result: outlier count iqr. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
baselineCount	Baseline Count	Required. Safe integer; minimum 4; maximum 1024.
baselineMean	Baseline Mean	Required. Finite number.
baselineStandardDeviation	Baseline Standard Deviation	Required. Finite number; minimum 0.
contaminationCount	Contamination Count	Required. Safe integer; minimum 0; maximum 128.
contaminationOffset	Contamination Offset	Required. Finite number; minimum 0.
contaminationSide	Contamination Side	Required. Text; one of "upper", "lower", "both".
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
values	Values	Required. Array of Finite number.; minimum items 4; maximum items 1152.
injected	Injected	Required. Array of Boolean.; minimum items 4; maximum items 1152.
injectedCount	Injected Count	Required. Safe integer; minimum 0; maximum 128.
detectedCount	Detected Count	Required. Safe integer; minimum 0; maximum 1152.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "baselineCount": 8,
    "baselineMean": 100,
    "baselineStandardDeviation": 5,
    "contaminationCount": 2,
    "contaminationOffset": 40,
    "contaminationSide": "both",
    "seed": 42
  },
  "output": {
    "values": [
      106.14295336531552,
      90.79186407608202,
      140,
      98.63486947558695,
      90.99103291156455,
      94.29558964522988,
      140,
      98.65741136686378,
      97.35283103141792,
      95.21918885307926
    ],
    "injected": [
      false,
      false,
      true,
      false,
      false,
      false,
      true,
      false,
      false,
      false
    ],
    "injectedCount": 2,
    "detectedCount": 2,
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Injected labels describe construction, not what the detector must flag. Baseline points may be outliers and contaminants may remain unflagged; no real-world anomaly or diagnostic performance is established.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Outlier Detection Test Case Generator — tool.io
Calculation or validation: statistics/outlier-count-iqr — tool.io

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